<abstract xmlns="http://www.w3.org/1999/xhtml"><p> A novel approach for instance selection in classification problems is presented. This adaptive instance selection is designed to simultaneously decrease the amount of computation resources required and increase the classification quality achieved. The approach generates new training samples during the evolutionary process and changes the training set for the algorithm. The instance selection is guided by means of changing probabilities, so that the algorithm concentrates on problematic examples which are difficult to classify. The hybrid fuzzy classification algorithm with a self-configuration procedure is used as a problem solver. The classification quality is tested upon 9 problem data sets from the KEEL repository. A special balancing strategy is used in the instance selection approach to improve the classification quality on imbalanced datasets. The results prove the usefulness of the proposed approach as compared with other classification methods.</p></abstract>

A novel approach for instance selection in classification problems is presented. This adaptive instance selection is designed to simultaneously decrease the amount of computation resources required and increase the classification quality achieved. The approach generates new training samples during the evolutionary process and changes the training set for the algorithm. The instance selection is guided by means of changing probabilities, so that the algorithm concentrates on problematic examples which are difficult to classify. The hybrid fuzzy classification algorithm with a self-configuration procedure is used as a problem solver. The classification quality is tested upon 9 problem data sets from the KEEL repository. A special balancing strategy is used in the instance selection approach to improve the classification quality on imbalanced datasets. The results prove the usefulness of the proposed approach as compared with other classification methods.

Self-Configuring Hybrid Evolutionary Algorithm for Fuzzy Imbalanced Classification with Adaptive Instance Selection

Institute of Computer Science and Telecommunications, Siberian State Aerospace University Krasnoyarskii rabochii ave. 31, 660014, Krasnoyarsk, Russian Federation

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

A novel approach for instance selection in classification problems is presented. This adaptive instance selection is designed to simultaneously decrease the...

Self-Configuring Hybrid Evolutionary Algorithm for Fuzzy Imbalanced Classification with Adaptive Instance Selection

Published Online: Jun 10, 2016

Page range: 173 - 188

DOI: https://doi.org/10.1515/jaiscr-2016-0013

Keywords
Fuzzy classification, instance selection, genetic fuzzy system, self-configuration

© 2016

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Self-Configuring Hybrid Evolutionary Algorithm for Fuzzy Imbalanced Classification with Adaptive Instance Selection

Published Online: Jun 10, 2016

Page range: 173 - 188

DOI: https://doi.org/10.1515/jaiscr-2016-0013

KeywordsFuzzy classification, instance selection, genetic fuzzy system, self-configuration

© 2016

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Keywords
Fuzzy classification, instance selection, genetic fuzzy system, self-configuration